For lenders, deciding which clients should receive a loan is a delicate tightrope that requires expert balance to navigate. On one hand, approving more clients generally results in greater revenues. However, clients’ applications represent varying levels of risk. Even worse, traditional methods of determining a potential borrower’s creditworthiness have become outdated and problematic.
A newly emerging solution aims to solve those problems: artificial intelligence (AI). The use of AI in financial services, just beginning to take off, is expected to be the future of the industry. Let’s examine how it’s different from previously accepted methods and how it can change the face of lending.
The Shortcomings of Traditional Credit Assessment
The main issue with traditional credit assessment methods is that they tend to over-rely on FICO scores, usually FICO 8. That number does have some value, but it doesn’t always represent the full picture. It’s an oversimplification that does a disservice to both parties.
Many pieces of context within a borrower’s life that illustrate their creditworthiness simply aren’t taken into account by standard credit measuring systems. AI-based lending algorithms take a deeper look to get a fuller picture of their present and future financial situation.
Traditional credit assessments also have issues with racial discrimination. According to a study posted by the Federal Reserve Board, Black and Hispanic applicants are less likely to receive home mortgages than white applicants with similar credit scores and histories. This is yet another problem that AI lending can resolve.
What Sets AI-Driven Lending Apart
Advanced models that incorporate AI-based algorithms into their programming generally consider many of the same criteria as their traditional counterparts, such as credit scores. However, they also use additional considerations like alternative data and behavioral patterns to determine creditworthiness.
Alternative data includes regular payments that are not usually covered but can also point to a person’s ability to pay, like utility bills. The standard FICO score doesn’t consider a borrower’s electricity, phone, or internet bills (among others) as evidence that they can make reliable monthly payments, but AI lending can.
Behavioral patterns include things like social media posting. If a person posts on Facebook, for example, that they recently bought a car on Craigslist for $5,000, AI can use posts by other people (e.g., the seller posting a picture of the same truck on Facebook Marketplace) to verify whether it’s true. If so, having enough money to either make payments or buy the car outright points to a creditworthy applicant.
Expanding the Approval Pool Responsibly
The main strength and desired end result of AI lending is to target and identify “invisible prime” borrowers. Those are creditworthy potential borrowers who would be able to repay their obligations but would be declined for various reasons through traditional methods.
According to a 2018 report by the Wharton School of the University of Pennsylvania, 22% of adult U.S. consumers have no credit score because their credit history isn’t detailed enough to qualify for one. These include recent immigrants, people who mainly make purchases with cash, people from backgrounds or cultures that discourage borrowing, and more.
Today, the power of AI lending makes it possible to look closer at the actual person involved, their overall financial situation, and their ability to repay potential loans. They’re no longer reduced to a number that isn’t even consistent between the three major credit bureaus.
How AI Mitigates Risk While Broadening Access
Some lenders may be concerned about the added risks involved with AI in financial services. With some applications, it’s a valid concern, but AI lending can reduce some of the risks inherent in the lending industry.
First, the continuous learning models AI uses keep the algorithm growing and expanding based on new information. Previously, systemic changes took months or even years. With AI, they can happen in real time as new information comes in.
AI also improves segmentation, allowing for both better-targeted advertising and tailored service packages. Since AI can better identify certain groups of invisible prime borrowers, it can also help determine what tools those groups need to succeed.
Finally, AI provides real-time fraud detection, which is essential for the entire finance industry, especially lenders. Thwarting fraudulent loan requests protects both lenders and their clients, as less money lost to fraud means there’s more money for qualified borrowers.
Real-World Impact for Lenders and Borrowers
The result of AI-based lending solutions is that portfolios can become larger and more inclusive without compromising performance. It truly represents a win-win arrangement, as there are massive benefits for both sides.
Borrowers can finally access loans that will fulfill their dreams, which never could have happened with traditional lending criteria. Instead of saving money for 10 years to start a business, they can begin the process now.
On the other side, lenders can diversify their portfolios and lower their overall risk by adding hidden prime borrowers. Instead of having the firm’s future rest with only a few major clients, they can significantly expand their borrowing pool and stay in business for years.
A Brighter Future for Both Sides
Most people think of AI simply as a tool for efficiency, but when it comes to finance, it’s so much more. It’s the future of the industry and a vital tool for gaining a strategic advantage in the quest for more inclusive, resilient lending programs.
Both the borrower and the lender benefit from the use of AI in financial services. Borrowers’ dreams can come true much faster than before. Lenders can lower their risk and add a whole new generation of brand-loyal clients who will use their services for many years to come.
